Systems and methods for intelligent ad-based routing

ABSTRACT

Examples of the present disclosure describe systems and methods for intelligent ad-based routing. In example aspects, a destination input, desired time for arrival, and user profile data is received in a ride-sharing application. The input data is classified by applying one or more machine-learning models to the data. Based on the classified data results, candidate physical advertisement locations may be selected along a certain route. Different types of routes may be selected that range from the shortest possible route (i.e., a direct route) to a major detour (i.e., the most cost-effective route). A major detour takes the user on a route that exposes the user to as many advertisements as possible while still arriving at the final destination before the desired time of arrival. In exchange for a longer route and more exposure to physical advertisements, the cost of the ride may be offset. While the user is in proximity to the physical advertisement along the route, businesses associated with the physical advertisement locations may transmit real-time advertisements/messages to the user to be displayed on a user device, a vehicular device, and/or a combination of the two. During the ride, the real-time business information may be received that may alter the route to maximize the effectiveness of real-time advertisement delivery.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a Continuation of U.S. Non-Provisional PatentApplication Ser. No. 16/573,529, filed Sep. 17, 2019, entitled “SYSTEMSAND METHODS FOR INTELLIGENT AD-BASED ROUTING,” the disclosure of whichis incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure is related to the field of intelligent routecreation based on advertisement exposure. In particular, the presentdisclosure is directed at a computer system that is capable of receivingdestination input data and user profile data and determining a routebased on the locations of physical advertisements, such as store fronts,billboards, bus stops, and the like. Along with the destination input, auser may also input a desired time of arrival (DTA). Based on the DTA,the computer systems and methods disclosed herein may create a certainroute that exposes the user to certain advertisements. In the context ofride-sharing and taxi-based applications, electing to take a longerroute to a destination with more advertisement exposure may decrease theoverall cost of the ride. In other words, a user may have the option toreceive discounted rides if the user is willing to take a longer routethat exposes the user to more ads. Additionally, a user may elect to payfull price for the ride, and the created route will be the quickestroute based on current traffic congestion and distance to thedestination. Application of intelligent ad-based routing decreases thecost of ride-sharing and taxi-based applications for consumers andshifts a portion of the cost to businesses wanting to advertise to theriders. Further, intelligent ad-based routing efficiently injectsrelevant advertisements, based on at least one machine-learningclassification algorithm, into a communication protocol utilized by auser device, vehicular computer, or other electronic device, amongothers.

BACKGROUND

With the proliferation of gig-economy ride-sharing and autonomousvehicles, fewer users will elect to manually drive themselves todestinations, opting instead to ride as a passenger in a car operated bya driver for a ride-sharing company and/or in a fully autonomous car. Asthe term is used herein, “ride-sharing” refers to an arrangement inwhich a passenger travels in a private vehicle that is operated by thevehicle's owner, typically arranged by means of a website or anapplication. The operation may be conducted through a human driver,autonomously, or a combination of both.

Over time, aggregated ride-sharing costs may become expensive for users.In order to offset the prices of ride-sharing, the systems and methodsdisclosed herein may reconfigure the route to the destination so thatthe rider-user is exposed to certain advertisements along the route. Inother words, businesses may offset the cost of ride-sharing by payingride-sharing companies to re-route their passengers to pass by physicaladvertisements, such as store fronts, billboards, bus stops, and similarphysical advertisements. The rider may elect a cheaper ride in exchangefor a longer route that passes by more physical advertisements.Conversely, the rider may elect a full-priced ride in exchange for thequickest, most direct route to the destination, without regard tophysical advertisement locations along the route.

After a user inputs the destination and DTA, the systems and methodsdisclosed herein may execute at least one classification algorithm todetermine the characteristics of the destination in relation to profiledata of the user. The classification data may be compared against atleast one machine learning model to further determine the most relevantadvertisements to display to the user. Relevance of advertisements maybe based on historical user data (e.g., trip data, purchase history,etc.) and/or real-time business information (e.g., whether the businessis currently open or closed, average busyness based on the current timeand day, special promotions or sales, etc.), among other data.

As such, the systems and methods disclosed herein enable businesses toleverage the proliferation of ride-sharing to inject intelligent andrelevant advertisements into a communication protocol as ride-sharingusers pass by physical advertisements in real-time. Rather thaninefficiently communicating advertisements without regard to a user'slocation or preferences, the systems and methods disclosed herein enablethe intelligent communication of real-time, relevant advertisements thatare cost-effective for both ride-sharing users and businesses.

It is with respect to these and other general considerations that theaspects disclosed herein have been made. Also, although relativelyspecific problems may be discussed, it should be understood that theexamples should not be limited to solving the specific problemsidentified in the background or elsewhere in the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive examples are described with reference tothe following figures.

FIG. 1 illustrates an example of a distributed system for intelligentadvertisement-based routing.

FIG. 2 illustrates an example method for creating an intelligentadvertisement-based route.

FIG. 3 illustrates an example method for classifying input data tointelligently select advertisement locations along a route.

FIG. 4 illustrates an example architecture of an input processing systemaccording to some embodiments of the disclosed technology.

FIG. 5A illustrates a Major Detour Route.

FIG. 5B illustrates a Direct Route.

FIG. 6 illustrates one example of a suitable operating environment inwhich one or more of the present embodiments may be implemented.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully below withreference to the accompanying drawings, which form a part hereof, andwhich show specific exemplary aspects. However, different aspects of thedisclosure may be implemented in many different forms and should not beconstrued as limited to the aspects set forth herein; rather, theseaspects are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of the aspects to those skilledin the art. Aspects may be practiced as methods, systems, or devices.Accordingly, aspects may take the form of a hardware implementation, anentirely software implementation or an implementation combining softwareand hardware aspects. The following detailed description is, therefore,not to be taken in a limiting sense.

Embodiments of the present application are directed at systems andmethods associated with intelligent computer systems that create routesfor ride-sharing passengers based in part on locations of physicaladvertisements and real-time business data associated with the physicaladvertisements. Additionally, the routes are intelligently created basedon user profile data and destination characteristics that can be, insome examples, extracted from at least one machine-learningclassification algorithm.

In an example, a user may request a ride from a ride-sharing service.The vehicle that eventually picks up the user may be a human-operatedvehicle, a fully autonomous vehicle, or a combination of the two. Theuser may input destination information and a desired time of arrival(DTA) into a user interface on an electronic device (e.g., mobile phone,laptop, etc.). The systems and methods described herein may receive thisdata and proceed to process the data to create an intelligent route.Based on the user profile data, the destination data, and the DTA data,the systems and methods described herein may first aggregate a list ofpotential advertisement locations along the route. Physicaladvertisements may include but are not limited to store fronts,billboards (e.g., electronic or non-electronic), signage, and transitads (e.g., bus-stop advertisements), among other types of physicaladvertisements. The systems and methods may then winnow down the list ofpotential advertising locations based on user preferences, historicaluser data, the distance to the destination, the classification of thedestination, and the DTA, among other factors. The application, whichexecutes the systems and methods described herein, may generate the mostcost-effective and relevant route for the user that still allows theuser to arrive at the destination by the DTA.

Although the intelligent route may be longer than the most direct route,the intelligent route will expose the user-passenger in the ride-sharingvehicle to specific physical advertisements during the route. The costof the ride-share to the user may be offset by the businesses wishing toadvertise with the ride-sharing company. In other examples, theapplication running the systems and methods described herein may be ableto communicate over a wireless network (e.g., 5G network) in real-timewith certain businesses as the user is approaching and passing thephysical advertisement locations. As a user approaches the GPS locationof a physical advertisement, a business associated with the physicaladvertisement may transmit a real-time message or advertisement to theuser's device (e.g., mobile phone). In other examples, the real-timemessage may be communicated directly to the ride-sharing vehicle anddisplayed within the vehicle on a screen (e.g., a screen mounted to theback of the driver or front-passenger seats, a screen embedded within awindow, etc.). When the ride has concluded, a cost offset may be appliedto the total trip charge that accounts for the level of advertisementexposure to the user. If the user elected to take a longer yet moreadvertisement-saturated ride, the user may pay less than a ride takingthe quickest, most direct path without regard to physical advertisementlocations and exposure.

Intelligent ad-based routing also improves the technical delivery ofreal-time electronic advertisements to devices. For instance, thesystems and methods disclosed herein allow businesses to transmitreal-time advertisements and messages to user devices based on the GPSlocation of the user devices. When a user is approaching or passing acertain physical location associated with a business, the geocoordinatesof the user may be used to trigger sending a real-time advertisement ormessage to the user device. In other examples, the geocoordinates may bebased on the vehicle in which the user is riding. For instance, in anautonomous vehicle with multiple interior screens, the geocoordinates ofthe vehicle may trigger the sending of a business's real-timeadvertisement or message to be displayed on the interior screens if thevehicle is within a defined physical proximity of a location associatedwith the particular business advertising within the vehicle.

By leveraging advanced wireless network capabilities, such as 5Gnetworking, businesses are able to more efficiently transmit targetedadvertisements to vehicular computers and user devices, while using lessbandwidth and decreasing network latency. Furthermore, the systems andmethods described herein are capable of delivering more relevantadvertisements to users based on technical processing improvements byleveraging advances in machine-learning technology. Specifically, byaggregating user profile data, destination classification data, and DTA,relevant advertisements can be intelligently selected and delivered tothe user in real-time.

It should be noted that the systems and methods described herein are notto be limited to just ride-sharing applications. A passenger riding in anon-ride-share vehicle (e.g., a user riding in a vehicle operated by theuser's spouse) may be able to use the systems and methods describedherein, as the advertisements can be directly sent to a user device orto a screen or screens located within the non-ride-share vehicle.

Current methods of electronic advertisement delivery do not considerphysical advertisement locations that may be along a driving route to aparticular destination. Rather, current methods rely on limited-targetedadvertising; for instance, in most ride-sharing applications, theadvertisements displayed in the application during the ride do notconsider the real-time geocoordinates of the user device and/or thevehicle. The advertisements displayed are generic and, often, have norelevance to the changing physical surroundings of the user and thevehicle during the ride or the final destination.

Accordingly, a need exists to deliver more targeted and relevantadvertisements through the creation of intelligent ad-based routes. Thepresent disclosure provides a plurality of benefits to the technicalinfrastructure associated with real-time, targeted advertisementdelivery, including but not limited to: decreasing the frequency ofgeneric, non-targeted advertisements which, overall, decreases networkbandwidth and latency; faster advertisement delivery using advancedwireless network technology (e.g., 5G) due to physical proximity of theuser device, vehicle, and physical advertisement; and enabling users toutilize ride-sharing services more frequently due to the offset cost ofbusinesses paying the ride-sharing company for targeted advertising,which will decrease traffic congestion on the roadways and improve thequality of roadways overall, among other examples.

FIG. 1 illustrates an example of a distributed system for creatingintelligent advertisement-based routes (“ad-based routes”). Examplesystem 100 presented is a combination of interdependent components thatinteract to form an integrated whole for creating ad-based routes.Components of the system may be hardware components or softwareimplemented on, and/or executed by, hardware components of the systems.For example, system 100 comprises client devices 102 and 104, vehiculardevice 106, local databases 110, 112, vehicular database 114, network(s)108, and server devices 116, 118, and/or 120.

Client devices 102 and 104 and vehicular device 106 may be configured toreceive destination input data, DTA data, and user profile data.Additionally, devices 102, 104, and 106 may be configured to communicatewith remote devices associated with third-party businesses over awireless network, such as network(s) 108. Devices 102, 104, and 106 mayalso be configured to communicate with other client devices andvehicular devices. In aspects, a client device and/or vehicular device,such as devices 102, 104, and 106, may have access to one or more datasources comprising data related to the destination input, user profile,and possible advertisement locations along a certain route. The datasources may be local to, or external to, the client devices and/orvehicular device. The data related to destination input may includeclassification data that describes certain categories and sub-categoriesof characteristics associated with the destination. A more fulsomeexplanation of the classification data associated with a destinationinput is presented with respect to FIG. 3 . The data related to a userprofile may include but are not limited to user preferences (e.g.,likes/dislikes), historical user data (e.g., past locations the user hasvisited), purchase history from third-party applications, advertisementinteraction history, and demographic information, among other categoriesof data. The data associated with DTA may include but is not limited tocurrent traffic data, geocoordinates of a particular route, andgeocoordinates of candidate advertisement locations in proximity to aparticular route.

As another example, the client devices and/or vehicular device mayreceive input data from the user and from various applicationprogramming interfaces (APIs) containing user profile data, destinationdata, and other relevant information. The data may be captured by thedevice and transmitted to one or more remote web servers or externaldata storage locations, such as server devices 116, 118, and/or 120. Theclient devices and/or vehicular device may be equipped with wirelesscards capable of joining and communicating over advanced wirelessnetworks, such as 5G.

In some aspects, the data that is received by the client/vehiculardevice(s) may be provided to one or more machine-learning (ML) models. Amodel, as used herein, may refer to a predictive or statistical utilityor program that may be used to determine a probability distribution overone or more character sequences, classes, objects, result sets orevents, and/or to predict a response value from one or more predictors.A model may be based on, or incorporate, one or more rule sets, machinelearning, a neural network, or the like. In an example, the ML modelsmay be installed on the client device, vehicular device, a serverdevice, a network appliance (e.g., a firewall, a router, etc.), or somecombination thereof. The ML models may process the data to determine themost relevant types of advertisements to display to a user, and based onsuch a determination, an intelligent ad-based route may be created.Determining which advertisements may be most relevant to a user maycomprise analyzing a user profile and comparing demographic informationof a user against a statistically significant dataset of individualswith similar demographic information. The determination of advertisementtype and content to display to a user can also be based on historicaluser data, such as the nature of a user's past interaction with acertain type, category, or subject matter of an advertisement. A usermay also select preferences manually, and the systems and methodsdescribed herein can obtain the user preference data and compare it toat least one ML model to extract further insights as to which types ofadvertisements may be most relevant and effective on a particular user.Based on the types of advertisements that are selected for the ad-basedroute, the client devices, vehicular device, and/or server device(s) mayperform (or cause the performance of) one or more actions.

In some aspects, the user profile data, destination characteristics, andpreviously selected advertisement locations may be used to train the oneor more ML models. For example, a set of labeled and/or unlabeled datamay be used to train an ML model to identify the most relevantadvertisements to a particular user. Relevance may be determined, insome examples, by considering how the user interacted with theadvertisement (e.g., whether the user clicked on the advertisement whenit appeared on the user's mobile device), the time the user spentengaged with the advertisement (e.g., how long a user watched anadvertisement displayed on a screen measured via eye-gaze trackingthrough a built-in camera), and whether the user's subsequent actionswere related to the advertisement (e.g., an advertisement for RestaurantX was displayed during the ad-based route, and the user subsequentlydined at Restaurant X within a certain period of time after beingexposed to the advertisement), among other examples. The training mayinclude the use of one or more supervised or unsupervised learningtechniques, including but not limited to pattern recognition techniques.The trained ML model may be deployed to one or more devices. As aspecific example, an instance of a trained ML model may be deployed to aserver device and to a client device. The ML model deployed to a serverdevice may be configured to be used by the client device and/orvehicular device when, for example, the client device and/or vehiculardevice is connected to a wireless network. Alternatively, the ML modeldeployed to the client device may be configured to be used by the clientdevice and/or vehicular device when, for example, the client deviceand/or vehicular device is not connected to the Internet. In suchexamples, the ML model may be locally cached by the client device and/orvehicular device.

FIG. 2 illustrates an example method for creating an intelligentadvertisement-based route. FIG. 2 begins at operation 202 where thedestination input is received by a client device (such as client devices102/104) and/or a vehicular device (such as vehicular device 106).Destination input may comprise an address and/or a name (e.g., name of acompany/business). Destination input may be manually input into anapplication user interface on a user device and/or a vehicular device,such as a touchscreen mounted to the back of a front driver or passengerseat in a vehicle. After destination input is received, the methodproceeds to operation 204 where desired time of arrival (DTA) data isreceived. Similar to how destination input may be received on aclient/vehicular device, DTA input may be received through anapplication user interface on a client device (e.g., through a mobileapplication) and/or a vehicular device (e.g., through a mountedtouchscreen inside the vehicle). The DTA data may be utilized todetermine the type of route to calculate. For instance, if the DTA is alater time than the time of arrival using a direct path to thedestination, then the ad-based routing system disclosed herein maydetermine that a longer, more advertisement-saturated route isappropriate. If the DTA is within a small period of time of thecalculated time of arrival using a direct path or if the DTA is earlierthan the calculated time of arrival using a direct path, the system mayautomatically default to calculating a direct path to the destinationwithout regard to an ad-based route. In some alternate embodiments, thecalculation of a direct route may still use advertisement preferencesand user profile data to calculate the best route. For instance, if twodirect routes are nearly similar in time, then the route that containsthe most relevant physical advertisements to the user may be selected bythe system.

Once the destination and DTA input are received, the method thenproceeds to operation 206 where the input data is classified. Inaddition to the destination input and DTA input received at operations202 and 204, input data may also comprise user profile data. Userprofile data may comprise user preferences (e.g., likes/dislikes),historical user actions (e.g., previous destinations, interactioncharacteristics with previously displayed advertisements), and otherrelevant data to creating an intelligent ad-based route. During step206, the destination may be classified. Specifically, the destinationmay be categorized based on industry, e.g., restaurant, grocery store,shoe store, doctor, etc. Sub-categories may also be used to furthercategorize a destination. For instance, within the restaurant category,a particular destination may be classified as a fast-food restaurant ora fine-dining restaurant. Similarly, a doctor's office may be classifiedas an optometrist or a chiropractor.

The DTA data may also be classified at operation 206. For instance, asdescribed earlier, the DTA may be within a small period of time of orearlier than the calculated time of arrival for a direct route to thedestination. In such instances, the DTA classification may take priorityover other categories of data classification, and the route that iscreated is the fastest route with minimal to no regard to advertisementpreferences. In other instances, the direct route to a destination maycomprise more than one route. Such cases may cause the system describedherein to compare the physical advertisement locations along each routeand intelligently select the one that is most relevant to the user basedon the destination classification and other user profile data.

At operation 206, user profile data may be received and classified alongwith the destination input data and DTA data. User profile data may beretrieved from previous information submitted to a user interfaceassociated with the systems and methods presented herein, or, inalternate embodiments, user profile data may be retrieved usingthird-party APIs (e.g., pulling user profile data from a social mediahost, such as Facebook® or LinkedIn®). The classification of the inputdata is described in more detail with respect to FIG. 3 .

Once the classification step is complete, potential advertisementlocations are received at operation 208. The potential advertisementlocations are intelligently selected at operation 206 based on theprocessing of the destination, DTA, and user data. The potentialadvertisement locations received at operation 208 may be ranked in orderof most relevant to least relevant based on the processed data fromoperation 206. The potential advertisement locations may also be rankedby relevance based on the type of route created in regard to the DTA(e.g., most relevant advertisement locations are the ones located alongthe roadways that comprise the most direct route to the destination).

After the potential advertisement locations are received, a route iscreated at operation 10. The ad-based route is created based on thedestination input, DTA, user profile data, and the received candidateadvertisement locations from operation 208. In one example aspect, theroute may be classified as a direct route, a minor detour, an averagedetour, a major detour, or free. The direct route may be defined as theshortest, possible route. On a direct route, physical advertisementlocations may or may not be present. As mentioned previously, however,multiple direct routes may exist. In such instances, the systems andmethods disclosed herein may take into account the classificationresults from operation 206 and choose the direct route that contains themost relevant advertisement locations to the user.

The detour routes (minor, average, and major) take the user by a set ofphysical advertisement locations that were intelligently selected atoperation 206 that the user otherwise would not have been exposed to ifthe user took a direct route to the destination. A minor detour route isa route that may contain a small amount of detour time (e.g., 5minutes). An average detour route is a route with an average amount ofdetour time (e.g., 10-15 minutes). A major detour route is a route witha large amount of detour time (e.g., 20-30 minutes). The larger theamount of detour time, the lower the cost of the ride because businessesare paying the ride-sharing company to drive the user past the physicaladvertisement locations associated with particular businesses. As such,a major detour provides a user the maximum amount of cost-savings for aride. A “free” route may be designed to take the user past as manyphysical advertisement locations as possible for the ride to be free ofcharge. Typically, a “free” route is created without regard to DTA data.

Once the initial ad-based route is created, the ride begins. During theride, the client device and/or vehicular device may receive data frombusinesses associated with the physical advertisements along the route.Real-time business data may be received that prompts a change in theroute to the destination (return to operation 210). For example, theinput data received and classified at operation 206 may become outdatedalong the route. Specifically, a business associated with a physicaladvertisement location along the route that was initially chosen atoperation 208 may be closed by the time the user is expected to pass bythe geocoordinate of the advertisement. In other instances, the businessitself may manually “turn-off” its physical advertisement locationshared with the ride-sharing application. The systems and methodsdisclosed herein are configured to allow businesses to register andde-register physical advertisement locations with the ride-sharingapplication. As such, when a business, for whatever reason, decides itno longer wants to pay the ride-sharing company to feature its candidatephysical advertisement location(s), the advertisement location(s) may bede-registered. During a ride, if a certain physical advertisementlocation was initially selected at operation 208 but the businesssubsequently de-registers its location during the ride but before thevehicle has approached and past the geocoordinates of the advertisementlocation in question, then the intelligent ad-based routing systemdisclosed herein may return to operation 210 and the ad-based route mayautomatically update, re-routing the vehicle and user to anotherphysical advertisement location that is currently registered with theride-sharing application.

In yet further examples, certain sensors associated with the physicalpremises of a business may indicate if a business is adequately busy ornot. Such sensors may transmit data in real-time to the intelligentad-based routing system, e.g., via wireless network(s) 108. Based onthis real-time data, the ad-based routing system may re-route thevehicle and user and proceed back to operation 210. For example, arestaurant may be experiencing higher than usual traffic. If therestaurant establishment was initially selected as a potentialadvertisement location at operation 208 and placed on the initial routeat operation 210, the ad-based routing system disclosed herein mayautomatically update the route prior to the vehicle and user approachingand passing the restaurant and its associated physical advertisement(s)(e.g., a restaurant sign or storefront). The updated route may betriggered because of the restaurant's level of busyness. If therestaurant is already at maximum capacity, then it may not want to spendmore money on real-time advertising via a ride-sharing application toattract customers to come in for a meal.

In such an example, the cost of the ride may dynamically change for theuser. Here, because the restaurant is no longer desiring to spend moneyon advertising (because the restaurant is at maximum capacity), the useris not going to receive a cost offset from the restaurant for the ride.Generally, the cost offset of a ride will be larger for a user when abusiness is at low capacity, and the cost offset will be smaller for auser when a business is at high capacity. Other variables that maydynamically affect the cost of a ride may include, but are not limitedto, the time of day, day of week, weather, surroundingactivities/events, and quality of external signage. For example, abusiness that opens at 11 am for lunch will likely advertise morebetween the hours of 11 am and 12 pm than between 12 pm and 1 pm (primelunch time), presumably because the traffic of customers will be lowerat 11 am and higher at 12 pm. As such, a user riding past the businessbetween 11 am and 12 pm will experience a greater dynamic cost offset tothe total cost of the ride than a user riding past the business between12 pm and 1 pm. Similarly, in a torrential downpour, visibility outsideof the vehicle may be obstructed. During such weather events, thedynamic cost offset may decrease because businesses do not want to paymore for advertising when the user-passenger in the vehicle cannot seethe storefront or sign of the business as the car drives past thestorefront/sign.

In other examples, surrounding activities and events may prompt dynamicchanges in the overall cost of a ride. For instance, if construction isoccurring in front of a business, the business may increase itsadvertising spend to compensate for the expected loss of traffic to thebusiness due to the construction. In such an instance, the dynamic costoffset may be larger than usual for a user riding past the businessbecause of the surrounding construction activity. Alternatively,construction in front of a business may prompt a business to decreaseits advertising spend if the business believes that the physicalconstruction surrounding the business will be a nuisance to newcustomers, and therefore, the business does not want to attract newcustomers during construction. As such, the dynamic cost offset may besmaller than usual for a user riding past the business because of thesurrounding construction activity. In another example, a conveniencestore might increase its advertising spend in the time leading up to alocal sporting event. For instance, on a hot summer day, prior to asporting event, many people may be traveling to a sports stadium via aride-share. The convenience store, which may be in proximity to thestadium, may increase its advertising spend during this time to promptthe sports fans to buy a water or sports drink prior to entering thestadium. As such, the dynamic cost offset of a user riding past theconvenience store may be larger during this time leading up to asporting event as compared to normal business hours when a sportingevent is not scheduled.

The process of receiving advertising and cost offset data frombusinesses along the route (and possibly cycling between operation 210and 212) continues until the vehicle arrives at the destination.

If the data received from a business along the route at operation 212does not prompt a change in the initial route, the method proceeds tooperation 214. At operation 214, a business may want to transmit areal-time advertisement and/or message to a client device associatedwith the user or a vehicular device inside of the vehicle. For example,one of the potential advertisement locations that may have been selectedat operation 208 is a clothing store. The data received from theclothing store at operation 212 may indicate to the intelligent ad-basedrouting system that the clothing store is open, operating at aslower-than-normal level of busyness, and currently offering a sale on aparticular style of t-shirt. As the vehicle is approaching the clothingstore, the user may look out of the vehicle and see the physicalstorefront of the clothing store. At the same time, the user may receive(on a client device 102/104 or on a vehicular device 106, such as ascreen within the vehicle) an advertisement and/or message from theclothing store. The advertisement and/or message from the clothing storereceived at operation 214 may take the form of a text-messageadvertisement, a visual advertisement (image or video) displayed on ascreen inside the vehicle, an audio advertisement broadcasted overspeakers inside the vehicle, augmented reality (AR) advertisements(e.g., the windows on the vehicle may be configured to display ARgraphics that overlay the outside physical world perceived from insideof the vehicle), and/or a combination of the aforementionedadvertisement types or any other feasible advertising medium.

In some instances, an advertisement displayed within the vehicle (e.g.,an image or video displayed on an in-vehicle screen) may prompt the userto purchase an actual product from inside the vehicle from adistribution mechanism built into the vehicle (e.g., a mobile vendingmachine). For example, in the context of the clothing storeadvertisement, the vending machine may be stocked with a certain t-shirtof various sizes that is sold at the clothing store. As the user isriding past the clothing store, the clothing store may transmit anadvertisement message via a shared wireless network (e.g., network(s)108) to vehicular device 106. The advertisement may be displayed on ascreen within the vehicle, prompting the user to purchase the particulart-shirt from the vending machine inside the vehicle. The user may acceptthe offer and purchase the t-shirt (e.g., via mobile paymentapplications and/or credit card), or the user may decline/ignore theoffer.

During the ride, electronic advertisements and/or messages from thebusinesses that are past along the route may continue to be received bythe client device(s) and/or vehicular device, at operation 214. Thisprocess continues until the vehicle and user arrive at the destination.Along the route, an advertisement may be displayed on a user's mobiledevice (e.g., mobile phone). The advertisement may be displayedaccording to the real-time GPS location of the user and an advertisementdata transmission rate. An advertisement data transmission rate is ameasurement of time for obtaining elements of an advertisement. Inlow-bandwidth networks, the advertisement data transmission rate isslow, whereas in high-bandwidth networks, the advertisement datatransmission rate is fast. The display of an advertisement on a clientdevice accounts for the real-time advertisement data transmission ratebased on the current strength of the surrounding wireless networks.

Once the vehicle and user arrive at the destination, the intelligentad-based system may proceed to end the ride at operation 216. Atoperation 216, the ride may be terminated automatically (e.g., based onarriving within a certain radius of the geocoordinates of thedestination, based on sensing the user physically exiting the vehicle,etc.) or manually (e.g., a human operator clicks “End Ride” on aride-sharing application). Upon conclusion of the ride, the user mayremit payment to the ride-sharing service via a secure payment method,such as a blockchain ledger or other secure payment means.

FIG. 3 illustrates an example method for classifying input data tointelligently select advertisement locations along a route. As can beappreciated by one of ordinary skill in the art, the operationsdescribed with respect to FIG. 3 may be performed locally, remotely, oron a combination of devices. For instance, all of the steps described inFIG. 3 may be performed locally on a client device, such as clientdevices 102 and/or 104. The method steps of FIG. 3 may also be performedsolely within a vehicular device, such as vehicular device 106. In otheraspects, the steps of FIG. 3 may be performed on one or more remotedevice, such as remote web server(s) 116, 118, and/or 120. In yet otherexamples, the steps of FIG. 3 may be performed on a combination ofclient devices, vehicular devices, and remote web servers allcommunicating over a shared wireless network, such as network(s) 108.

FIG. 3 begins at operations 302 and 304, where destination input dataand user profile data are received by a device. The data may be receivedby a client device, vehicular device, or a remote web server device. Inthe instance of a remote web server, the destination input data and userprofile data may be received initially from a client device and/orvehicular device and then transmitted via a wireless network(network(s)108) to a remote web server device, where the remote webserver device receives the destination input and user profile data. Thedestination data and user profile data may be converted into particulardata representations that may be understood and processed by a machineutilizing machine-learning algorithms (e.g., ML Engine 306) tointelligently disassemble the destination and user profile data andprovide the most appropriate physical advertisement locations along theroute.

The destination data and user profile data may be transmitted toMachine-Learning (ML) Engine 306, where the data may be used to train atleast one ML model and/or compared against an already-trained ML modelor models. In some aspects, the first operation in ML Engine 306 may beextract features operation 308. At operation 308, certain features maybe extracted from the destination data and user profile data, includingbut not limited to contextual features and lexical features. Forinstance, the lexical features that may be analyzed include, but are notlimited to, word n-grams that may appear in social media status updates,text messages, emails, and/or other text-based user profile data. A wordn-gram is a contiguous sequence of n words from a given sequence oftext. For instance, a particular social media update earlier in the dayfrom the user may state: “Enjoyed getting lunch with Bob this afternoon.The burger was great!” The word n-gram that may be extracted in thisinstance is “burger was great.” The intelligent ad-based routing systemnow knows that the user has already eaten a burger for lunch, andtherefore, the system may de-prioritize restaurant advertisementsdisplaying hamburgers during the creation of the route. In other words,restaurants serving burgers may be less optimal advertisement locationsto display to a user who has already eaten a burger earlier that sameday. As should be appreciated, analyzing word n-grams may allow for adeeper understanding of the user and therefore provide more accurate andintelligent advertising location suggestions to display along a route.The machine-learning algorithms from ML Engine 306 may be able tocompare thousands of n-grams, lexical features, and contextual featuresin a matter of seconds to extract the relevant features of a socialmedia, text, and/or email message. Such rapid comparisons are impossibleto employ manually.

The contextual features that may be analyzed at operation 308 mayinclude, but are not limited to, a top context and an average context. Atop context may be a context that is determined by comparing the topicsand keywords of text-based input data (e.g., social media update, textmessage, email, destination description on a search engine, etc.). to aset of preloaded contextual cues. An average context may be a contextthat is determined by comparing the topics and keywords of historicalprocessed text-based input data, historical advertisement location dataand advertisement interaction data, user profile data (e.g., scrapedfrom publicly facing social media websites), and other data. The featureextraction operation 308 may also skip contextually insignificant datawhen analyzing the input. For example, a string token in a message inputmay be associated with articles, such as “a” and “an.” However, becausearticles are typically insignificant in the English language, thefeature extraction operation 308 may ignore these article tokens.

In other examples, the destination input data 302 may comprise images ofthe restaurant. These images may be provided by the destination to apublic search engine (e.g., Google®), or these images may be provided byprevious customers to a public business directory service (e.g., Yelp®).ML Engine 306 may be configured to not only process text-based data, butalso process image data. At operation 308, features that may beextracted from an image include, but are not limited to, objects. Theseobjects may be identified at operation 308 by applying an imagerecognition algorithm stored within ML Engine 306. The image recognitionalgorithm may be able to identify objects, such as food items from arestaurant destination, clothing items from a clothing store, andelectronics products from a technology store, among other examples.

Once the features are extracted at operation 308, the domain of theinput is classified at operation 310. The features that were extractedat operation 308 may be grouped together into specific classifiers forfurther analysis at operation 310. Specifically, classifying the domainof the extracted features at operation 310 may utilize statisticalmodels or predefined policies (i.e., prior knowledge, historicaldatasets) to determine the proper domain of classification. For example,if a user input a golf store as the final destination into theride-sharing application, one of the features that may have beenextracted at operation 308 was a golf club object (from an image) and/ora word n-gram describing “golf clubs, shoes, and accessories.” Atoperation 310, the word “golf” may be associated with a broader domainclassification such as a “sports” domain.

At operation 312, the intent of traveling to the destination may bedetermined. For example, historical user data may indicate that the useris not a frequent golfer, so the system may assume the likelihood of theuser going to a golf store to purchase an object for him/herself is low.Considering the user profile data 304 and the features extracted atoperation 308 (e.g., via public social media postings), however, revealsthat the brother of the user is a golf aficionado. Further, the dataextracted from the user profile data indicates that the user's brother'sbirthday is next week. As such, an intent for traveling to thedestination may be assumed as “golf gift for brother's birthday.” Itshould be appreciated that multiple intents may be predicted atoperation 312.

After the features are extracted at operation 308, domains classified atoperation 310, and an intent or intents determined at operation 312, thesystem may determine candidate advertisement locations to display alonga route to the destination at operation 314. Continuing from the earliergolf example, the user may be traveling to a golf shop as the finaldestination. As previously mentioned, a possible domain classificationfor the golf shop is “sports.” As such, other businesses with physicaladvertisements along the possible route that are also classified as“sports” domains may be more relevant advertisements to display to theuser. In other aspects, however, based on the determined intent fromoperation 312, the user may be traveling to the golf shop to purchase abirthday present for his/her brother. In such an instance, the possibleadvertisement location(s) determined at operation 314 may prioritizelocations such as a cake shop and/or a card store over “sports” stores.

Additionally, at operation 314, the advertisement locations that aredetermined along the route may also be determined using historicaladvertisement data 318. For instance, if a particular advertisementalong a certain route has elicited low engagement from the user, thenthat advertisement may not be selected to be displayed along the route.In other examples, certain domains of physical advertisements may have ahistorically low engagement rate from the user. Such physicaladvertisements will have a lower priority of being selected to bedisplayed along a route as compared to high-engagement advertisements.In some instances, no historical advertisement data may be available fora particular route or a particular user. In such instances, defaultadvertisement location data 316 may be utilized in determining whichadvertisements to display along a particular route. The defaultadvertisement location data may be based on the highest-payingbusinesses to the ride-sharing service, in some examples. In otherinstances, the default advertisement data may be based on averagecustomer ratings of publicly available business directory services orsearch engines.

After possible advertisement locations are selected at operation 314,the method proceeds to optional step 320 where the application issynchronized with the selected advertisement locations. Step 320 occursduring a re-routing (e.g., cycling between operation 212 and operation210 from FIG. 2 ). After an initial route is programmed into theapplication, any changes to that route may require the application tosynchronize to the new determined advertisement locations. As such, thenew advertisement locations are provided to the application, and theapplication is synchronized at operation 320.

Following the determination of the advertisement locations at operation314 and/or the synchronization of the application operation 320, theadvertisement locations and/or synchronized application data areprovided to advertisement location manager 324 that generates a route atoperation 326. Based on the DTA data and determined advertisementlocations, a route is generated at operation 326. After the route isgenerated at operation 326, the route data is provided at operation 328to the application running on the client device 102/104 and/or vehiculardevice 106 via wireless network(s) 108. The generated route data anddetermined advertisement locations are also provided to a database 322where advertisement location and route data are stored and used toupdate the historical advertisement database 318. The updated historicaladvertisement database 318 is then used in future calculations ofdetermining the most relevant and appropriate advertisements to displayalong a route to a user.

FIG. 4 illustrates an example architecture of an input processing systemaccording to some embodiments of the disclosed technology. Inputprocessing system 400 may be embedded within a client device (e.g.,client devices 102 or 104) and/or a vehicular device (e.g., vehiculardevice 400) and/or on a remote web server device (e.g., devices 116,118, and/or 120). The input processing system contains one or more dataprocessors and is capable of executing algorithms, software routines,and/or instructions based on processing data provided by a variety ofsources related to the intelligent selection of an ad-based route. Theinput processing system can be a factory-fitted system or an add-on unitto a particular device. Furthermore, the input processing system can bea general-purpose computer or a dedicated, special-purpose computer. Nolimitations are imposed on the location of the input processing systemrelative to a client, vehicular, and/or remote web server device.According to embodiments shown in FIG. 4 , the disclosed system caninclude memory 405, one or more processors 410, classification module415, advertisement location module 420, communications module 425, androute guidance module 430. Other embodiments of the present technologymay include some, all, or none of these modules and components, alongwith other modules, applications, data, and/or components. Still yet,some embodiments may incorporate two or more of these modules andcomponents into a single module and/or associate a portion of thefunctionality of one or more of these modules with a different module.

Memory 405 can store instructions for running one or more applicationsor modules on processor(s) 410. For example, memory 405 could be used inone or more embodiments to house all or some of the instructions neededto execute the functionality of classification module 415, advertisementlocation module 420, communications module 425, and route guidancemodule 430. Generally, memory 405 can include any device, mechanism, orpopulated data structure used for storing information. In accordancewith some embodiments of the present disclosures, memory 405 canencompass, but is not limited to, any type of volatile memory,nonvolatile memory, and dynamic memory. For example, memory 405 can berandom access memory, memory storage devices, optical memory devices,magnetic media, floppy disks, magnetic tapes, hard drives, SIMMs, SDRAM,RDRAM, DDR, RAM, SODIMMs, EPROMs, EEPROMs, compact discs, DVDs, and/orthe like. In accordance with some embodiments, memory 405 may includeone or more disk drives, flash drives, one or more databases, one ormore tables, one or more files, local cache memories, processor cachememories, relational databases, flat databases, and/or the like. Inaddition, those of ordinary skill in the art will appreciate manyadditional devices and techniques for storing information that can beused as memory 405.

Classification module 415 may be configured to run a portion of theoperational steps described in FIG. 3 . Module 415 receives destinationdata and user profile data and transmits that data to a machine learningengine. The machine-learning engine may be housed within classificationmodule 415. After the input data is processed through themachine-learning engine, the data is classified based on extractedfeatures, domains, and intent. In some embodiments, classificationmodule 415 may be configured with more than one machine-learning engine,so that processing of the input data can occur in tandem, andclassification results can be produced faster. In some embodiments, theclassification module 415 is configured to communicate with third-partyapplications and application programming interfaces. Such communicationmay allow classification module 415 to receive certain user profile datathat may not be available natively through the intelligent ad-basedrouting application.

Advertisement location module 420 is configured to receive theclassification data from classification module 415 and subsequentlylocate the most relevant and appropriate physical advertisements todisplay along a route to a user. Advertisement location module 420 maybe configured to access local and/or remote databases that containhistorical advertisement location and interaction information. Forinstance, advertisement location module 420 may rely on historical dataabout a user's engagement level with a particular type of advertisementin determining the physical advertisements to display along the route.Further, advertisement location module 420 is configured to retrievedefault advertisement data from a local and/or remote database in thecase that historical data is unavailable for a particular user and/orroute.

Communications module 425 is associated with sending/receivinginformation (e.g., collected and classified by classification module415) with other client devices, vehicular devices, and/or remoteservers. These communications can employ any suitable type oftechnology, such as Bluetooth, WiFi, WiMax, cellular (e.g., 5G), singlehop communication, multi-hop communication, Dedicated Short RangeCommunications (DSRC), or a proprietary communication protocol. In someembodiments, communications module 425 sends information classified byclassification module 415 and advertisement location data fromadvertisement location module 420.

Route guidance module 430 is associated with the functionality ofdriving a vehicle on a prescribed ad-based route, including modifyingthe route of the vehicle based on real-time business information that isreceived by classification module 415. As described previously,real-time business data may be received by the ad-based routing systemdisclosed herein. Based on the received business data, the ad-basedroute may be updated in real-time. For instance, a business may beclosed by the time the user and vehicle are scheduled to pass thephysical advertisement associated with that business, and therefore, theintelligent ad-based routing system changes the route so that the useris exposed to a physical advertisement of a business that is currentlyopen. In some embodiments, route guidance module 430 may be associatedwith the driving functionality of an autonomous vehicle, ensuring thatthe autonomous vehicle is driving according to a path calculated by theintelligent ad-based routing system according to data classified byclassification module 415 and geocoordinates of physical advertisementsdetermined by advertisement location module 420.

FIG. 5A illustrates a Major Detour Route. In one example route, a usermay be picked up at start location 502. Prior to pick-up, a user mayenter a destination and DTA into a user interface associated with theintelligent ad-based routing application described herein. Thedestination and DTA data may be processed along with user profile datato create the most relevant, cost-effective ad-based route. Based on theDTA submitted by the user, the ad-based routing system may determinethat a major detour route is the most relevant and cost-effective route,since the user will still arrive at final destination 514 at or beforethe DTA taking the major detour route. The major detour route may takethe user past physical advertisements 504, 506, 508, 510, and 512 beforereaching final destination 514. Physical advertisements 504, 506, 508,510, and 512 may have been determined by the intelligent ad-basedrouting system using classification data of the destination, historicaldata about the user, and other relevant reference points.

During the ride, the user may have received various advertisementsand/or messages from businesses associated with advertisement locations504, 506, 508, 510, and 512. As previously described, theseadvertisements/messages may have been communicated over a sharedwireless 5G network (such as network(s) 108) and transmitted directly toa user device and/or vehicular device.

FIG. 5B illustrates a Direct Route. In other examples, based on thedestination input and DTA, the intelligent ad-based routing system mayselect the shortest possible route for the user—a direct route. Thedirect route, as illustrated, only takes the user past physicaladvertisement 512 and partially, advertisement 504. In exchange for lessexposure to physical advertisements and quicker arrival times, a usermay pay a higher price for a direct route as compared to a detour route,such as a major detour route illustrated in FIG. 5A.

FIG. 6 illustrates one example of a suitable operating environment inwhich one or more of the present embodiments may be implemented. This isonly one example of a suitable operating environment and is not intendedto suggest any limitation as to the scope of use or functionality. Otherwell-known computing systems environments, and/or configurations thatmay be suitable for use include, but are not limited to, personalcomputers, server computers, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, programmable consumer electronicssuch as smart phones, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

In its most basic configuration, operating environment 600 typicallyincludes at least one processing unit 602 and memory 604. Depending onthe exact configuration and type of computing device, memory 604(storing, among other things, information related to detected devices,advertisement data, association information, personal gateway settings,and instructions to perform the methods disclosed herein) may bevolatile (such as RAM), non-volatile (such as ROM, flash memory, etc.),or some combination of the two. This most basic configuration isillustrated in FIG. 6 by dashed line 606. Further, environment 600 mayalso include storage devices (removable 608 and/or non-removable 610)including, but not limited to, magnetic or optical disks or tape.Similarly, environment 600 may also have input device(s) 614 such askeyboard, mouse, pen, voice input, vehicular sensors, etc. and/or outputdevice(s) 616 such as a display, speakers, printer, vehicular parts(e.g., wheels, transmission, etc.), etc. Also included in theenvironment may be one or more communication connections, 612, such as5G, Bluetooth, WiFi, WiMax, LAN, WAN, point to point, etc.

Operating environment 600 typically includes at least some form ofcomputer readable media. Computer readable media can be any availablemedia that can be accessed by processing unit 602 or other devicescomprising the operating environment. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, RAM, ROM EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage, orother magnetic storage devices, or any other tangible medium which canbe used to store the desired information. Computer storage media doesnot include communication media.

Communication media embodies computer readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of any ofthe above should also be included within the scope of computer readablemedia.

The operating environment 600 may be a single computer (e.g., mobilecomputer and/or vehicular computer) operating in a networked environmentusing logical connections to one or more remote computers. The remotecomputer may be a personal computer, a server, a router, a network PC, apeer device, a vehicular computer, or other common network node, andtypically includes many or all of the elements described above as wellas others not so mentioned. The logical connections may include anymethod supported by available communications media. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet.

Aspects of the present disclosure, for example, are described above withreference to block diagrams and/or operational illustrations of methods,systems, and computer program products according to aspects of thedisclosure. The functions/acts noted in the blocks may occur out of theorder as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved.

The description and illustration of one or more aspects provided in thisapplication are not intended to limit or restrict the scope of thedisclosure as claimed in any way. The aspects, examples, and detailsprovided in this application are considered sufficient to conveypossession and enable others to make and use the best mode of theclaimed disclosure. The claimed disclosure should not be construed asbeing limited to any aspect, example, or detail provided in thisapplication. Regardless of whether shown and described in combination orseparately, the various features (both structural and methodological)are intended to be selectively included or omitted to produce anembodiment with a particular set of features. Having been provided withthe description and illustration of the present application, one skilledin the art may envision variations, modifications, and the alternateaspects falling within the spirit of the broader aspects of the generalinventive concept embodied in this application that do not depart fromthe broader scope of the claimed disclosure.

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thescope of the invention. Accordingly, the invention is not limited exceptas by the appended claims.

1. A computer-implemented method associated with intelligentadvertisement-based routing comprising: processing a destination, adesired time of arrival data, and user profile data associated with auser to form processed data; comparing the processed data to at leastone database of advertisements geographically located in proximity tothe destination; based on the comparison of the processed data to the atleast one database of advertisements, generating a firstadvertisement-based route and an estimated time of arrival associatedwith the first advertisement-based route; receiving location dataassociated with at least one ride-share vehicle transporting the userand synchronized business data associated with at least oneadvertisement location along the first advertisement-based route; basedon the location data, generating a second advertisement-based route; anddynamically re-routing the at least one ride-share vehicle to the secondadvertisement-based route .
 2. The method of claim 1, furthercomprising: recording at least one user interaction with at least oneadvertisement associated with the second advertisement-based route; andsaving the at least one user interaction in a database for futureprocessing.
 3. The method of claim 2, wherein the at least one userinteraction is measured according to at least one of: an engagementtime, a click, an eye-gaze duration, and a subsequent purchase.
 4. Themethod of claim 1, further comprising presenting at least oneadvertisement associated with the second advertisement-based route, themethod further comprises determining at least one advertisement displaytime, wherein the advertisement display time is dynamically calculatedbased on at least one of: a current location of the user, the at leastone advertisement location, and an advertisement data transmission rate.5. The method of claim 1, further comprising receiving destination inputdata, wherein the destination input data comprises at least one of: anaddress, a GPS location, a description, an hours of operation, and acustomer rating.
 6. The method of claim 1, wherein the user profile datacomprises at least one of: social media profile data, at least one userpreference, text message data, email data, contacts data, GPS locationdata, and historical advertisement interaction data.
 7. The method ofclaim 5, further comprising, extracting features from the destinationinput data.
 8. The method of claim 7, wherein the extracted featurescomprise at least one of: contextual features and lexical features. 9.The method of claim 8, further comprising classifying at least onedomain associated with the contextual features or lexical features of atleast one of the destination input data and the user profile data. 10.The method of claim 9, further comprising determining at least one userintent based on the domain classification of at least one of thedestination input data and the user profile data.
 11. The method ofclaim 1, further comprising analyzing historical advertisement dataassociated with the second advertisement-based route, wherein thehistorical advertisement data comprises at least one of: a past userinteraction history with the at least one advertisement location and anoverall statistical interaction summary of the at least oneadvertisement based on multiple users.
 12. A system comprising: at leastone processor; and memory coupled to the at least one processor, thememory comprising computer executable instructions that, when executedby the at least one processor, performs a method comprising: processinga destination, a desired time of arrival data, and user profile dataassociated with a user to form processed data. comparing the processeddata to at least one database of advertisements geographically locatedin proximity to the destination; based on the comparison of theprocessed data to the at lease one database of advertisements,generating a first advertisement-based route and an estimated time ofarrival associated with the first advertisement-based route; receivinglocation data associated with at least one ride-share vehicletransporting the user and synchronized business data associated with atleast one advertisement location along the first advertisement-basedroute; based on the location data, generating a secondadvertisement-based route; and dynamically re-routing the at least oneride-share vehicle to the second advertisement-based route.
 13. Themethod of claim 12, further comprising receiving input data, wherein theinput data comprises at least one of: an image, a video, a social mediapost, a destination address, a destination description, destinationgeocoordinates, a user preference, and a user profile.
 14. The method ofclaim 12, further comprising receiving real-time business information,wherein the real-time business information comprises at least one of: aclosure notification, an opening notification, a surrounding activityindicator, a quality of external signage ranking, an hourly busynessindicator, and a day-of-week busyness indicator.
 15. The method of claim12, wherein the first advertisement-based rote is one of: a directroute, a minor detour, an average detour, a major detour, and a freeroute.
 16. The method of claim 12, further comprising receiving, on auser device, at least one electronic advertisement associated with thefirst advertisement-based route, wherein the user device is in proximityto the at least one advertisement location.
 17. The method of claim 16,wherein the user device comprises at least one of: a mobile phone, atablet, a laptop, and a vehicular device.
 18. The method of claim 16,wherein the at least one electronic advertisement comprises at least oneof: a mobile advertisement, a static image advertisement, a videoadvertisement, an audio advertisement, an augmented-realityadvertisement, and an in-vehicle vending machine advertisement.
 19. Avehicular computer comprising: a memory; a processor coupled to thememory, wherein the processor is configured to: process a destination, adesired time of arrival data, and user profile data associated with auser to form processed data; compare the processed data to at least onedatabase of advertisements geographically located in proximity to thedestination; based on the comparison of the processed data to the atleast one database of advertisements, generating a firstadvertisement-based route and an estimated time of arrival associatedwith the first advertisement-based route; receive location dataassociated with at least one ride-share vehicle transporting the userand synchronized business data associated with at least oneadvertisement location along the first advertisement-based route; basedon the location data, generate a second advertisement-based route, anddynamically re-route the at least one ride-share vehicle to the secondadvertisement-based route.
 20. The vehicular computer of claim 19,further comprising a route guidance module, wherein the route guidancemodule is configured to guide an autonomous vehicle according to thefirst advertisement-based route.